---
abstractspacing: double
fontsize: 12pt
margin: 2cm
urlcolor: darkblue
linkcolor: Mahogany
citecolor: Mahogany
spacing: single
bibliography: references.bib
biblio-style: apalike
output:
  pdf_document:
    citation_package: natbib
    fig_caption: no
    number_sections: no
    keep_tex: no
    toc: no
    toc_depth: 3
    template: article-template.latex
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE,cache=TRUE)
knitr::opts_chunk$set(fig.width=7, fig.height=6, out.width = '70%', fig.align = "center") 
rm(list=ls())
library(remotes)
library(kableExtra)
library(haven)
library(tidyverse)
library(ivmodel)
library(doParallel)
library(foreach)
library(estimatr)
require(AER)
library(lfe)
library(glue)
#path <- "/Users/ziwenzu/Dropbox/research/IV/IV Sensitivity/LLXZ_rep"
path <- "~/Dropbox/ProjectZ/IV Sensitivity/LLXZ_rep"
setwd(path)
knitr::opts_knit$set(root.dir = path)
#install_github("apoorvalal/ivDiag")
library(ivDiag)
# number of cores
cores <- 15
```

## @hong2022strongman

| Replication Summary | |
|---------|---------------------|
| Unit of analysis |township |
| Treatment | NVM subsidy per voter |
| Instrument | Terrain elevation slope |
| Outcome | Park’s vote share in 2012 |
| Model | Table3(3)|


```{r ajps_Hong_2022}
df <-readRDS("./rawdata/ajps_Hong_etal_2022.rds")
df<-as.data.frame(df)
D<-"total_Lamount_1974_1978_perelect" 
Y <- "E18ConsSh"
Z <- c("te_median1", "ts_median1")
controls <- c("area_1970","demo_female_share_1966","demo_age_15plus_1966",
              "demo_illiterate_1966","demo_pop_ch_1970_1966","E17ConsSh","eup")
cl <- "CTY_cd"
FE <- "CTY_cd"
weights<-NULL
(g<-ivDiag(data=df, Y=Y, D=D, Z=Z, controls=controls, FE =FE, 
  cl =cl,weights=weights, cores = cores))
```

```{r, cache = FALSE}
plot_coef(g)
```

```{r ajps_Hong_2022_sav, echo = FALSE}
save(g, file="./estimate/Hong2022.RData")
```


